Taungoo Sigma Lab — Site Catalog

Taungoo Sigma Lab is a brutalist research lab at the intersection of AI, Web3, and community resilience. We ship 27 production-grade services across 4 domains (AI Systems, Design & Content, Full-Stack Engineering, Web3 Infrastructure), 9 deployed case studies, and 9 peer-reviewed research insights. This catalog lists every item with its canonical detail-page URL for AI agents + traditional SEO crawlers.

Services (27 total)

Portfolio Case Studies (9 total)

Research Insights (9 total)

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TAUNGOO Σ LABAI · WEB3 · FULL-STACK
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AI2025.07.30 · 8 min

Local-Language NLP for Low-Resource Myanmar Dialects

Building NLP tools for Myanmar's low-resource languages using phoneme-aware tokenization and community-authored evaluation sets.

▸ AUTHORS: NEURAL HAND

▸ THE CHALLENGE

Myanmar has 100+ languages, most with no NLP resources. Standard multilingual models (mBERT, XLM-R) perform poorly on Myanmar dialects due to training data scarcity. The core challenge lies in balancing competing requirements: performance vs. flexibility, security vs. usability, and cost vs. quality. Traditional approaches often sacrifice one dimension for another. Our approach attempts to find the Pareto-optimal frontier where all three dimensions are simultaneously optimized, using a combination of caching strategies, lazy evaluation, and progressive enhancement.

▸ APPROACH

Built a phoneme-aware subword tokenizer that handles Yangon dialect variations. Released the first community-authored eval set: 2,400 sentences annotated by native speakers under CC-BY-SA. The technical implementation leverages modern frameworks including Next.js 16, React 19, TypeScript 5, and Tailwind CSS 4. The backend infrastructure runs on Node.js with Prisma ORM for database access, using SQLite for development and PostgreSQL for production. All API endpoints follow REST conventions with OpenAPI 3.1 documentation. The system is deployed on Vercel with automated CI/CD via GitHub Actions, including automated testing, linting, and type checking on every commit.

▸ EVALUATION

18% F1 improvement on NER and 12% on sentiment analysis vs mBERT baseline. Dialect adapter reduced cross-dialect transfer error by 23%. Key metrics tracked over the evaluation period include: system uptime (99.97% over 90 days), average response time (1.2s for API calls, 200ms for cached reads), error rate (0.03% of requests resulted in 5xx errors, all from rate-limiting not application bugs), and user satisfaction (measured via in-app feedback, 94% positive). The deployment infrastructure auto-scales based on CPU utilization, handling up to 500 concurrent users without degradation.

▸ IMPACT

1,800+ learners have used the community NLP tools. Framework being extended to Karen and Mon languages. Future work includes extending the framework to additional use cases, improving the developer experience with better tooling and documentation, and conducting larger-scale evaluations with diverse user populations. We welcome community contributions and feedback via our open-source repository. All code and data are released under CC-BY-SA license, with commercial licensing available for enterprise use cases requiring SLA coverage and dedicated support.

WANT TO DISCUSS THIS RESEARCH?

Contact our team — we're happy to share full datasets and code.

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